Abstract
The Unmanned Aerial Vehicle (UAV) proliferation has raised many concerns, since their potentially malicious usage renders them as a detrimental tool for a number of illegal activities. Radar based counter-UAV applications provide a robust solution for UAV detection and classification. Most of the existing research addresses the problem of UAV classification by extracting features from the time variations of the Fourier spectra. Yet, these solutions require that the UAV is illuminated by the radar for a longer time which can be only met by a tracking radar architecture. On the other hand, surveillance radar architectures don’t have such a cumbersome requirement and are generally superior in maintaining situational awareness, due their ability for constantly searching on a 360\(^{\circ }\) area for targets. Nevertheless, the available automatic UAV classification methods for this type of radar sensors are relatively inefficient. This work proposes the incorporation of the deep learning paradigm in the classification pipeline, to provide an alternative UAV classification method that can handle data from a surveillance radar. Therefore, a Deep Neural Network (DNN) model is employed to discern between UAVs and negative examples (e.g. birds, noise, etc.). The conducted experiments demonstrate the validity of the proposed method, where the overall classification accuracy can reach up to \(95.0\%\).
This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement N\(^{\circ }\) 740859, ALADDIN.
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References
Knott, E.F., Schaeffer, J.F., Tulley, M.T.: Radar Cross Section. SciTech Publishing (2004)
Molchanov, P., Harmanny, R.I., de Wit, J.J., Egiazarian, K., Astola, J.: Classification of small UAVs and birds by micro-doppler signatures. Int. J. Microwave Wirel. Technol. 6(3–4), 435–444 (2014)
Sullivan, R.: Radar Foundations for Imaging and Advanced Concepts. The Institution of Engineering and Technology (2004)
Tait, P.: Introduction to Radar Target Recognition. vol. 18. IET (2005)
Chen, V.C., Li, F., Ho, S.S., Wechsler, H.: Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electron. Syst. 42(1), 2–21 (2006)
De Wit, J., Harmanny, R., Molchanov, P.: Radar micro-doppler feature extraction using the singular value decomposition. In: International Radar Conference 2014, pp. 1–6. IEEE (2014)
de Wit, J.M., Harmanny, R., Premel-Cabic, G.: Micro-doppler analysis of small UAVs. In: 9th European Radar Conference 2012, pp. 210–213. IEEE (2012)
Oh, B.S., Guo, X., Wan, F., Toh, K.A., Lin, Z.: Micro-Doppler mini-UAV classification using empirical-mode decomposition features. IEEE Geosci. Remote Sens. Lett. 15(2), 227–231 (2017)
Harmanny, R., De Wit, J., Cabic, G.P.: Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram. In: 11th European Radar Conference 2014, pp. 165–168. IEEE (2014)
Kim, B.K., Kang, H.S., Park, S.O.: Drone classification using convolutional neural networks with merged doppler images. IEEE Geosci. Remote Sens. Lett. 14(1), 38–42 (2016)
Ghadaki, H., Dizaji, R.: Target track classification for airport surveillance radar (ASR). In: 2006 IEEE Conference on Radar, 4 pp. IEEE (2006)
Chen, W., Liu, J., Li, J.: Classification of UAV and bird target in low-altitude airspace with surveillance radar data. Aeronaut. J. 123(1260), 191–211 (2019)
Mohajerin, N., Histon, J., Dizaji, R., Waslander, S.L.: Feature extraction and radar track classification for detecting UAVs in civillian airspace. In: IEEE Radar Conference 2014, pp. 0674–0679. IEEE (2014)
Namatēvs, I.: Deep convolutional neural networks: structure, feature extraction and training. Inf. Technol. Manag. Sci. 20(1), 40–47 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Mendis, G.J., Randeny, T., Wei, J., Madanayake, A.: Deep learning based doppler radar for micro UAS detection and classification. In: 2016 IEEE Military Communications Conference MILCOM 2016, pp. 924–929. IEEE (2016)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Dizaji, R.M., Ghadaki, H.: Classification system for radar and sonar applications. US Patent 7,567,203, July 2009
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp. 91–99 (2015)
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Special thanks to IDS Ingegneria Dei Sistemi S.p.A. for providing their radar sensor, the signal processing knowledge and the assistance in the dataset creation.
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Samaras, S., Magoulianitis, V., Dimou, A., Zarpalas, D., Daras, P. (2019). UAV Classification with Deep Learning Using Surveillance Radar Data. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_68
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